TY - GEN
T1 - Learning pairwise-similarity guided sparse functional connectivity network for MCI Classification
AU - Chen, Xiaobo
AU - Zhang, Han
AU - Zhang, Yu
AU - Li, Zuoyong
AU - Shen, Dinggang
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported in part by NIH grants (EB006733, EB008374, AG041721, AG049371, AG042599, AG053867, EB022880), the National Natural Science Foundation of China (Grant No. 61203244, 61773184, 61772254), Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (MJUKF201724), and the Talent Foundation of Jiangsu University, China (No. 14JDG066).
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.
AB - Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.
KW - Functional connectivity
KW - Mild cognitive impairment
KW - Resting-state fMRI
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85060542977&partnerID=8YFLogxK
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U2 - 10.1109/ACPR.2017.147
DO - 10.1109/ACPR.2017.147
M3 - Conference contribution
AN - SCOPUS:85060542977
T3 - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
SP - 923
EP - 928
BT - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th Asian Conference on Pattern Recognition, ACPR 2017
Y2 - 26 November 2017 through 29 November 2017
ER -